Cognee is an AI memory platform that stores information in a self-hosted knowledge graph so agents can retain context across sessions. It ingests data, connects related information, and helps agents retrieve it for reasoning and actions. The catalogue includes skills and instructions that extend agent workflows around Cognee.
Borrowing it
Nothing to install: this file belongs to topoteretes/cognee. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/topoteretes/cognee/main/.claude/skills/cognee-integrations/SKILL.mdgit clone --depth 1 https://github.com/topoteretes/cogneeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/topoteretes/cognee/cognee-integrations)<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-integrations"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-integrations.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01090 |
| Opus 5 | $0.00036 | $0.00545 |
| Sonnet 5 | $0.00014 | $0.00218 |
| Haiku 4.5 | $0.00007 | $0.00109 |
Grade A, and why
cognee-integrations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set up cognee integrations
All integration config is environment variables (.env). The authoritative,
always-current list with commented examples is .env.template at the repo
root — check it before inventing variable names. Install the matching extra
before switching a backend (e.g. pip install cognee[postgres]).
LLM providers
Default is OpenAI (LLM_API_KEY is all you need). To switch, set
LLM_PROVIDER, LLM_MODEL, LLM_API_KEY, and (where relevant)
LLM_ENDPOINT / LLM_API_VERSION:
- Azure OpenAI:
LLM_PROVIDER=azure,LLM_MODEL=azure/gpt-4o-mini, endpoint + api version required. - Gemini (no extra needed):
LLM_PROVIDER=gemini,LLM_MODEL=gemini/gemini-2.0-flash-exp. - Anthropic (
cognee[anthropic]):LLM_PROVIDER=anthropic, model e.g.claude-3-5-sonnet-20241022. - Ollama, local (
cognee[ollama]):LLM_PROVIDER=ollama,LLM_ENDPOINT=http://localhost:11434/v1, and set the embedding block +HUGGINGFACE_TOKENIZERtoo. - Custom / OpenRouter / vLLM:
LLM_PROVIDER=customwith the provider's OpenAI-compatible endpoint. - AWS Bedrock (
cognee[aws]):LLM_PROVIDER=bedrock+ AWS credentials/region.
The classic trap: LLM and embeddings are configured independently
(EMBEDDING_PROVIDER, EMBEDDING_MODEL, EMBEDDING_ENDPOINT,
EMBEDDING_API_KEY). Configuring only one leaves the other on OpenAI —
either keep a valid OpenAI key or configure both.
Databases
- Relational (
DB_PROVIDER): sqlite (default) or postgres (cognee[postgres]; host/port/user/password/name viaDB_*vars). - Vector (
VECTOR_DB_PROVIDER): lancedb (default), pgvector (cognee[postgres], needsVECTOR_DB_URL), neptune_analytics (cognee[neptune]), turso (cognee[turso]). Anything else (ChromaDB, Qdrant, Weaviate, Milvus, …) lives in community adapters — install from https://github.com/topoteretes/cognee-community and register withuse_vector_adapterbefore use; settingVECTOR_DB_PROVIDERalone raises "Unsupported vector database provider". - Graph (
GRAPH_DATABASE_PROVIDER): ladybug (default), neo4j (cognee[neo4j], bolt URL + credentials), neptune (cognee[neptune]), ladybug-remote, postgres (no raw Cypher / natural-language search).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 74 lines · 72 tokens per session scan A b9c18daf592c
cognee-integrations is a skill published in the GitHub repository topoteretes/cognee (30,542 stars, last pushed yesterday), licensed Apache-2.0. It adds 72 tokens to every session and 1,090 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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